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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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High-fidelity image transmission and reconstruction through multimode fiber using OAM modes and deep learning.

Jiaqi Wang, Hu Zhang, Shengxi Zeng

|February 20, 2026

Summary

Researchers encoded images using orbital angular momentum (OAM) mode superpositions in multimode optical fibers. Advanced neural networks achieved high-fidelity image reconstruction, reaching up to 99% accuracy with OAM filtering enhancing edge details.

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Area of Science:

Optics and Photonics
Artificial Intelligence
Computational Imaging

Background:

Multimode optical fibers offer high spatial-mode capacity for image transmission.
Environmental instabilities and complex inputs create speckle, hindering accurate image reconstruction.
Orbital Angular Momentum (OAM) offers a novel approach to encoding information in light.

Purpose of the Study:

To develop a high-fidelity image reconstruction method using OAM mode superpositions in multimode optical fibers.
To enhance image reconstruction accuracy and edge fidelity using deep learning techniques.
To demonstrate the generalization capabilities of the developed models across different optical conditions.

Main Methods:

Encoding grayscale images into OAM mode superpositions.
Utilizing a ResNet-based decoding network with transfer learning for image reconstruction.
Developing an attention-enhanced DoubleU-Net for reconstructing images with complex edge structures.
Implementing OAM filtering to improve edge fidelity.

Main Results:

Achieved up to 99% reconstruction accuracy using the ResNet model with transfer learning, showing generalization across wavelengths.
The attention-enhanced DoubleU-Net improved reconstruction accuracy by approximately 4% for complex images with rich edge structures, reaching 95% accuracy.
OAM filtering was experimentally verified to substantially enhance edge fidelity.

Conclusions:

OAM mode superpositions combined with deep learning provide a robust method for high-fidelity image reconstruction in multimode optical fibers.
The developed neural network architectures demonstrate strong generalization and improved performance for complex image features.
This work paves the way for advanced applications in optical information processing, communication, and imaging.